{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# scikit-learn → PMML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporter: LinearSVC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Set used: auto_mpg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Steps:    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### - Build the Pipeline with model and pre-processing (tf-idf vectorizer) using sklearn LinearSVC\n",
    "##### - Build PMML using Nyoka exporter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model building (using pipeline) for auto-mpg Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('mapper', DataFrameMapper(default=False, df_out=False,\n",
       "        features=[(['mpg', 'displacement', 'horsepower'], [MinMaxScaler(copy=True, feature_range=(0, 1))]), (['weight', 'acceleration'], [StandardScaler(copy=True, with_mean=True, with_std=True)]), ('car name', TfidfVectorizer(analyzer='...ax_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0))])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.preprocessing import MinMaxScaler,StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "\n",
    "df = pd.read_csv('auto-mpg.csv')\n",
    "\n",
    "X = df.drop(['cylinders','model year','origin'],axis=1)\n",
    "y = df['cylinders']\n",
    "feature_names = X.columns\n",
    "target_name = \"cylinders\"\n",
    "\n",
    "\n",
    "pipeline_obj = Pipeline([\n",
    "    ('mapper', DataFrameMapper([\n",
    "        (['mpg','displacement','horsepower'],[MinMaxScaler()]),\n",
    "        (['weight','acceleration'],[StandardScaler()]),\n",
    "        ('car name', TfidfVectorizer())\n",
    "    ])),\n",
    "    ('model',LinearSVC())\n",
    "])\n",
    "\n",
    "pipeline_obj.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Export the Pipeline object into PMML using the Nyoka package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nyoka import skl_to_pmml\n",
    "skl_to_pmml(pipeline_obj,feature_names,target_name,\"lsvc_tfidf_pmml.pmml\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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